A convolutional neural network model for SOH estimation of Li-ion batteries with physical interpretability

This publication has 31 references indexed in Scilit:

• Nonlinear-drifted Brownian motion with multiple hidden states for remaining useful life prediction of rechargeable batteries
  Mechanical Systems and Signal Processing, 2017
• State of Charge and State of Health Estimation for Lithium Batteries Using Recurrent Neural Networks
  IEEE Transactions on Vehicular Technology, 2017
• Charging, power management, and battery degradation mitigation in plug-in hybrid electric vehicles: A unified cost-optimal approach
  Mechanical Systems and Signal Processing, 2017
• Particle-filtering-based failure prognosis via sigma-points: Application to Lithium-Ion battery State-of-Charge monitoring
  Mechanical Systems and Signal Processing, 2017
• Learning Deep Features for Discriminative Localization
  2016
• Accommodating lithium into 3D current collectors with a submicron skeleton towards long-life lithium metal anodes
  Nature Communications, 2015
• Battery Health Prognosis for Electric Vehicles Using Sample Entropy and Sparse Bayesian Predictive Modeling
  IEEE Transactions on Industrial Electronics, 2015
• Online identification of lithium-ion battery parameters based on an improved equivalent-circuit model and its implementation on battery state-of-power prediction
  Journal of Power Sources, 2015
• Intelligent prognostics for battery health monitoring based on sample entropy
  Expert Systems with Applications, 2011
• Recurrence Plots of Dynamical Systems
  Europhysics Letters, 1987